Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle.

BACKGROUND: Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results. RESULTS: Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (-log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability. CONCLUSION: Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

Integrating computer vision algorithms and RFID system for identification and tracking of group-housed animals: an example with pigs.

Precision livestock farming aims to individually and automatically monitor animal activity to ensure their health, well-being, and productivity. Computer vision has emerged as a promising tool for this purpose. However, accurately tracking individuals using imaging remains challenging, especially in group housing where animals may have similar appearances. Close interaction or crowding among animals can lead to the loss or swapping of animal IDs, compromising tracking accuracy. To address this challenge, we implemented a framework combining a tracking-by-detection method with a radio frequency identification (RFID) system. We tested this approach using twelve pigs in a single pen as an illustrative example. Three of the pigs had distinctive natural coat markings, enabling their visual identification within the group. The remaining pigs either shared similar coat color patterns or were entirely white, making them visually indistinguishable from each other. We employed the latest version of the You Only Look Once (YOLOv8) and BoT-SORT algorithms for detection and tracking, respectively. YOLOv8 was fine-tuned with a dataset of 3,600 images to detect and classify different pig classes, achieving a mean average precision of all the classes of 99%. The fine-tuned YOLOv8 model and the tracker BoT-SORT were then applied to a 166.7-min video comprising 100,018 frames. Results showed that pigs with distinguishable coat color markings could be tracked 91% of the time on average. For pigs with similar coat color, the RFID system was used to identify individual animals when they entered the feeding station, and this RFID identification was linked to the image trajectory of each pig, both backward and forward. The two pigs with similar markings could be tracked for an average of 48.6 min, while the seven white pigs could be tracked for an average of 59.1 min. In all cases, the tracking time assigned to each pig matched the ground truth 90% of the time or more. Thus, our proposed framework enabled reliable tracking of group-housed pigs for extended periods, offering a promising alternative to the independent use of image or RFID approaches alone. This approach represents a significant step forward in combining multiple devices for animal identification, tracking, and traceability, particularly when homogeneous animals are kept in groups.

Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling.

The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly impacts the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (i) infer the mediation effects of the rumen microbiome on feed efficiency traits, (ii) estimate the direct and total heritability of feed efficiency traits, and (iii) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), while the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7 to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability <0.10 but significant causal effects on feed efficiency are attractive for external interventions. On the other hand, 2 groups of microbes with heritability ≥0.10, significant causal effects, and genetic covariances and causal effects with the same or opposite sign to feed efficiency are attractive for selective breeding, improving or decreasing the trait heritability and response to selection, respectively. In general, the inclusion of the different microbes in genomic models tends to decrease the trait heritability rather than increase it, ranging from -15% to +5%, depending on the microbial group and phenotypic trait. Our findings provide more understanding to target rumen microbes that can be manipulated, either through selection or management interventions, to improve feed efficiency traits.

Genetic analysis of feed efficiency and novel feeding behavior traits measured in group-housed broilers using electronic feeders.

This study aimed to estimate genetic parameters for feeding behavior (FB) traits and to assess their genetic relationship with performance traits in group-housed broilers. In total, 99,472,151 visits were recorded for 95,711 birds between 2017 and 2022 using electronic feeders. The visits were first clustered into 2,667,617 daily observations for ten FB traits: daily feed intake (DFI), daily number of visits (NVIS), time spent at the feeders (TSF), number of visited feeders (NVF), visiting activity interval (VAI), feeding rate (FR), daily number of meals (NMEAL), average intake per meal (INTMEAL), number of visits per meal (VISMEAL) and interval between meals (MEALIVL). All FB traits were then considered as the average per bird across the feeding test period. Three growth traits (body weight at the start - SBW and at the end of the feeding test - FBW, and weight gain over the test period - BWG), and 2 feed efficiency (FE) traits (Feed Conversion Rate - FCR and Residual Feed Intake - RFI) were also recorded. The (co)variance components were estimated using multitrait animal mixed models. For growth and FE, the heritability (h2) estimates were moderate, ranging from 0.20 ± 0.01 (BWG) to 0.32 ± 0.02 (RFI). Overall, the h2 estimates for FB traits were higher than for productive traits, ranging from 0.31 ± 0.01 (DFI) to 0.56 ± 0.02 (TSF). DFI presented high genetic correlations (0.53-0.86) with all performance traits. Conversely, the remaining FB traits presented null to moderate genetic correlations with these traits, ranging from -0.38 to 0.42 for growth traits and between -0.14 and 0.25 for FE traits. Genetic selection for favorable feeding behavior is expected to exhibit a fast genetic response. The results suggest that it is possible to consider different feeding strategies without compromising the genetic progress of FE. Conversely, breeding strategies prioritizing a higher bird activity might result in lighter broiler lines in the long term, given the negative genetic correlations between visit-related traits (NV, NVF, and NMEAL) and growth traits (SBW and FBW).

Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.

Machine learning methods for genomic prediction of cow behavioral traits measured by automatic milking systems in North American Holstein cattle.

Identifying genome-enabled methods that provide more accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In this study, we aimed to compare the predictive performance of traditional genomic prediction methods and deep learning algorithms for genomic prediction of milking refusals (MREF) and milking failures (MFAIL) in North American Holstein cows measured by automatic milking systems (milking robots). A total of 1,993,509 daily records from 4,511 genotyped Holstein cows were collected by 36 milking robot stations. After quality control, 57,600 SNPs were available for the analyses. Four genomic prediction methods were considered: Bayesian least absolute shrinkage and selection operator (LASSO), multiple layer perceptron (MLP), convolutional neural network (CNN), and GBLUP. We implemented the first 3 methods using the Keras and TensorFlow libraries in Python (v.3.9) but the GBLUP method was implemented using the BLUPF90+ family programs. The accuracy of genomic prediction (mean square error) for MREF and MFAIL was 0.34 (0.08) and 0.27 (0.08) based on LASSO, 0.36 (0.09) and 0.32 (0.09) for MLP, 0.37 (0.08) and 0.30 (0.09) for CNN, and 0.35 (0.09) and 0.31(0.09) based on GBLUP, respectively. Additionally, we observed a lower reranking of top selected individuals based on the MLP versus CNN methods compared with the other approaches for both MREF and MFAIL. Although the deep learning methods showed slightly higher accuracies than GBLUP, the results may not be sufficient to justify their use over traditional methods due to their higher computational demand and the difficulty of performing genomic prediction for nongenotyped individuals using deep learning procedures. Overall, this study provides insights into the potential feasibility of using deep learning methods to enhance genomic prediction accuracy for behavioral traits in livestock. Further research is needed to determine their practical applicability to large dairy cattle breeding programs.

Image analysis to automatically classify anemia based on Famacha score in sheep using ocular conjunctiva images.

Haemonchus contortus is the most pathogenic blood-feeding parasitic in sheep, causing anemia and consequently changes in the color of the ocular conjunctiva, from the deep red of healthy sheep to shades of pink to practically white of non-healthy sheep. In this context, the Famacha method has been created for detecting sheep unable to cope with the infection by H. contortus, through visual assessment of ocular conjunctiva coloration. Thus, the objectives of this study were (1) to extract ocular conjunctiva image features to automatically classify Famacha score and compare two classification models (multinomial logistic regression-MLR and random forest-RF) and (2) to evaluate the applicability of the best classification model on three sheep farms. The dataset consisted of 1,156 ocular conjunctiva images from 422 animals. RF model was used to segment the images, i.e., to select the pixels that belong to the ocular conjunctiva. After segmentation, the quantiles (1%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 99%) of color intensity in each image channel (red, blue, and green) were determined and used as explanatory variables in the classification models, and the Famacha scores 1 (non-anemic) to 5 (severely anemic) were the target classes to be predicted (scores 1 to 5, with 162, 255, 443, 266, and 30 images, respectively). For objective 1, the performance metrics (precision and sensitivity) were obtained using MLR and RF models considering data from all farms randomly split. For objective 2, a leave-one-farm-out cross-validation technique was used to assess prediction quality across three farms (farms A, B, and C, with 726, 205, and 225 images, respectively). The RF provided the best performances in predicting anemic animals, as indicated by the high values of sensitivity for Famacha score 3 (80.9%), 4 (46.2%), and 5 (60%) compared to the MLR model. The precision of the RF was 72.7% for Famacha score 1 and 62.5% for Famacha score 2. These results indicate that is possible to successfully predict Famacha score, especially for scores 2 to 4, in sheep via image analysis and RF model using ocular conjunctiva images collected in farm conditions. As expected, model validation excluding entire farms in cross-validation presented a lower prediction quality. Nonetheless, this setup is closer to reality because the developed models are supposed to be used across farms, including new ones, and with different environments and management conditions.

Genetic architecture and polygenic risk score prediction of degenerative suspensory ligament desmitis (DSLD) in the Peruvian Horse.

Introduction: Spontaneous rupture of tendons and ligaments is common in several species including humans. In horses, degenerative suspensory ligament desmitis (DSLD) is an important acquired idiopathic disease of a major energy-storing tendon-like structure. DSLD risk is increased in several breeds, including the Peruvian Horse. Affected horses have often been used for breeding before the disease is apparent. Breed predisposition suggests a substantial genetic contribution, but heritability and genetic architecture of DSLD have not been determined. Methods: To identify genomic regions associated with DSLD, we recruited a reference population of 183 Peruvian Horses, phenotyped as DSLD cases or controls, and undertook a genome-wide association study (GWAS), a regional window variance analysis using local genomic partitioning, a signatures of selection (SOS) analysis, and polygenic risk score (PRS) prediction of DSLD risk. We also estimated trait heritability from pedigrees. Results: Heritability was estimated in a population of 1,927 Peruvian horses at 0.22 ± 0.08. After establishing a permutation-based threshold for genome-wide significance, 151 DSLD risk single nucleotide polymorphisms (SNPs) were identified by GWAS. Multiple regions of enriched local heritability were identified across the genome, with strong enrichment signals on chromosomes 1, 2, 6, 10, 13, 16, 18, 22, and the X chromosome. With SOS analysis, there were 66 genes with a selection signature in DSLD cases that was not present in the control group that included the TGFB3 gene. Pathways enriched in DSLD cases included proteoglycan metabolism, extracellular matrix homeostasis, and signal transduction pathways that included the hedgehog signaling pathway. The best PRS predictive performance was obtained when we fitted 1% of top SNPs using a Bayesian Ridge Regression model which achieved the highest mean of R2 on both the probit and logit liability scales, indicating a strong predictive performance. Discussion: We conclude that within-breed GWAS of DSLD in the Peruvian Horse has further confirmed that moderate heritability and a polygenic architecture underlies the trait and identified multiple DSLD SNP associations in novel tendinopathy candidate genes influencing disease risk. Pathways enriched with DSLD risk variants include ones that influence glycosaminoglycan metabolism, extracellular matrix homeostasis, signal transduction pathways.

Exploring associations among morphometric measurements, genetic group of sire, and performance of beef on dairy calves.

Sire selection for beef on dairy crosses plays an important role in livestock systems as it may affect future performance and carcass traits of growing and finishing crossbred cattle. The phenotypic variation found in beef on dairy crosses has raised concerns from meat packers due to animals with dairy-type carcass characteristics. The use of morphometric measurements may help to understand the phenotypic structures of sire progeny for selecting animals with greater performance. In addition, due to the relationship with growth, these measurements could be used to early predict the performance until the transition from dairy farms to sales. The objectives of this study were 1) to evaluate the effect of different beef sires and breeds on the morphometric measurements of crossbred calves including cannon bone (CB), forearm (FA), hip height (HH), face length (FL), face width (FW) and growth performance; and (2) to predict the weight gain from birth to transition from dairy farms to sale (WG) and the body weight at sale (BW) using such morphometric measurements obtained at first days of animals' life. CB, FA, HH, FL, FW, and weight at 7 ±â€…5 d (BW7) (Table 1) were measured on 206 calves, from four different sire breeds [Angus (AN), SimAngus (SA), Simmental (SI), and Limousin (LI)], from five farms. To evaluate the morphometric measurements at the transition from dairy farms to sale and animal performance 91 out of 206 calves sourced from four farms, and offspring of two different sires (AN and SA) were used. To predict the WG and BW, 97 calves, and offspring of three different sires (AN, SA, and LI) were used. The data were analyzed using a mixed model, considering farm and sire as random effects. To predict WG and BW, two linear models (including or not the morphometric measurements) were used, and a leave-one-out cross-validation strategy was used to evaluate their predictive quality. The HH and BW7 were 7.67% and 10.7% higher (P < 0.05) in SA crossbred calves compared to AN, respectively. However, the ADG and adjusted body weight to 120 d were 14.3% and 9.46% greater (P < 0.05) in AN compared to SA. The morphometric measurements improved the model's predictive performance for WG and BW. In conclusion, morphometric measurements at the first days of calves' life can be used to predict animals' performance in beef on dairy. Such a strategy could lead to optimized management decisions and greater profitability in dairy farms.

Blastocyst Cell Number and Allocation Affect the Developmental Potential and Transcriptome of Bovine Somatic Cell Nuclear Transfer Embryos.

Cloning cattle using somatic cell nuclear transfer (SCNT) is inefficient. Although the rate of development of SCNT embryos in vitro is similar to that of fertilized embryos, most fail to develop into healthy calves. In this study, we aimed to identify developmentally competent embryos according to blastocyst cell composition and perform transcriptome analysis of single embryos. Transgenic SCNT embryos expressing nuclear-localized HcRed gene at day 7 of development were imaged by confocal microscopy for cell counting and individually transferred to recipient heifers. Pregnancy rates were determined by ultrasonography. Embryos capable of establishing pregnancy by day 35 had an average of 117 ± 6 total cells, whereas embryos with an average of 128 ± 5 cells did not establish pregnancy (P < 0.05). A lesser average number of 41 ± 3 cells in the inner cell mass (ICM) also resulted in pregnancies (<0.05) than a greater number of 48 ± 2 cells in the ICM. Single embryos were then subjected to RNA sequencing for transcriptome analysis. Using weighted gene coexpression network analysis, we identified clusters of genes in which gene expression correlated with the number of total cells or ICM cells. Gene ontology analysis of these clusters revealed enriched biological processes in coenzyme metabolic process, intracellular signaling cascade, and glucose catabolic process, among others. We concluded that SCNT embryos with fewer total and ICM cell numbers resulted in greater pregnancy establishment rates and that these differences are reflected in the transcriptome of such embryos.

Longitudinal analysis of bovine mammary gland development.

Many studies on bovine mammary glands focus on one stage of development. Often missing in those studies are repeated measures of development from the same animals. As milk production is directly affected by amount of parenchymal tissue within the udder, understanding mammary gland growth along with visualization of its structures during development is essential. Therefore, analysis of ultrasound and histology data from the same animals would result in better understanding of mammary development over time. Thus, this research aimed to describe mammary gland development using non-invasive and invasive tools to delineate growth rate of glandular tissue responsible for potential future milk production. Mammary gland ultrasound images, biopsy samples, and blood samples were collected from 36 heifer dairy calves beginning at 10 weeks of age, and evaluated at 26, 39, and 52 weeks. Parenchyma was quantified at 10 weeks of age using ultrasound imaging and histological evaluation, and average echogenicity was utilized to quantify parenchyma at later stages of development. A significant negative correlation was detected between average echogenicity of parenchyma at 10 weeks and total adipose as a percent of histological whole tissue at 52 weeks. Additionally, a negative correlation between average daily gain at 10 and 26 weeks and maximum echogenicity at 52 weeks was present. These results suggest average daily gain and mammary gland development prior to 39 weeks of age is associated with development of the mammary gland after 39 weeks. These findings could be predictors of future milk production, however this must be further explored.

Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep.

This study investigated the feasibility of using easy-to-measure phenotypic traits to predict sheep resistant, resilient, and susceptible to gastrointestinal nematodes, compared the classification performance of multinomial logistic regression (MLR), linear discriminant analysis (LDA), random forest (RF), and artificial neural network (ANN) methods, and evaluated the applicability of the best classification model on each farm. The database comprised 3654 records of 1250 Santa Inês sheep from 6 farms. The animals were classified into resistant (2605 records), resilient (939 records), and susceptible (110 records) according to fecal egg count and packed cell volume. A random oversampling method was performed to balance the dataset. The classification methods were fitted using the information of age class, the month of record, farm, sex, Famacha© degree, body weight, and body condition score as predictors, and the resistance, resilience, and susceptibility to gastrointestinal nematodes as the target classes to be predicted considering data from all farms randomly. An additional leave-one-farm-out cross-validation technique was used to assess prediction quality across farms. The MLR and LDA models presented good performances in predicting susceptible and resistant animals. The results suggest that the use of readily available records and easily measurable traits may provide useful information for supporting management decisions at the farm level.

Across-breed genetic investigation of canine hip dysplasia, elbow dysplasia, and anterior cruciate ligament rupture using whole-genome sequencing.

Here, we report the use of genome-wide association study (GWAS) for the analysis of canine whole-genome sequencing (WGS) repository data using breed phenotypes. Single-nucleotide polymorphisms (SNPs) were called from WGS data from 648 dogs that included 119 breeds from the Dog10K Genomes Project. Next, we assigned breed phenotypes for hip dysplasia (Orthopedic Foundation for Animals (OFA) HD, n = 230 dogs from 27 breeds; hospital HD, n = 279 dogs from 38 breeds), elbow dysplasia (ED, n = 230 dogs from 27 breeds), and anterior cruciate ligament rupture (ACL rupture, n = 279 dogs from 38 breeds), the three most important canine spontaneous complex orthopedic diseases. Substantial morbidity is common with these diseases. Previous within- and between-breed GWAS for HD, ED, and ACL rupture using array SNPs have identified disease-associated loci. Individual disease phenotypes are lacking in repository data. There is a critical knowledge gap regarding the optimal approach to undertake categorical GWAS without individual phenotypes. We considered four GWAS approaches: a classical linear mixed model, a haplotype-based model, a binary case-control model, and a weighted least squares model using SNP average allelic frequency. We found that categorical GWAS was able to validate HD candidate loci. Additionally, we discovered novel candidate loci and genes for all three diseases, including FBX025, IL1A, IL1B, COL27A1, SPRED2 (HD), UGDH, FAF1 (ED), TGIF2 (ED & ACL rupture), and IL22, IL26, CSMD1, LDHA, and TNS1 (ACL rupture). Therefore, categorical GWAS of ancestral dog populations may contribute to the understanding of any disease for which breed epidemiological risk data are available, including diseases for which GWAS has not been performed and candidate loci remain elusive.

Analysis of the causal structure of traits involved in sow lactation feed efficiency.

BACKGROUND: Feed efficiency during lactation involves a set of phenotypic traits that form a complex system, with some traits exerting causal effects on the others. Information regarding such interrelationships can be used to predict the effect of external interventions on the system, and ultimately to optimize management practices and multi-trait selection strategies. Structural equation models can be used to infer the magnitude of the different causes of such interrelationships. The causal network necessary to fit structural equation models can be inferred using the inductive causation (IC) algorithm. By implementing these statistical tools, we inferred the causal association between the main energy sources and sinks involved in sow lactation feed efficiency for the first time, i.e., daily lactation feed intake (dLFI) in kg/day, daily sow weight balance (dSWB) in kg/day, daily litter weight gain (dLWG) in kg/day, daily back fat thickness balance (dBFTB) in mm/day, and sow metabolic body weight (SMBW) in kg0.75. Then, we tested several selection strategies based on selection indices, with or without dLFI records, to improve sow efficiency during lactation. RESULTS: The IC algorithm using 95% highest posterior density (HPD95%) intervals resulted in a fully directed acyclic graph, in which dLFI and dLWG affected dSWB, the posterior mean of the corresponding structural coefficients (PMλ) being 0.12 and - 0.03, respectively. In turn, dSWB influenced dBFTB and SMBW, with PMλ equal to 0.70 and - 1.22, respectively. Multiple indirect effects contributed to the variances and covariances among the analyzed traits, with the most relevant indirect effects being those involved in the association between dSWB and dBFTB and between dSWB and SMBW. Selection strategies with or without phenotypic information on dLFI, or that hold this trait constant, led to the same pattern and similar responses in dLFI, dSWB, and dLWG. CONCLUSIONS: Selection based on an index including only dBFTB and dLWG records can reduce dLFI, keep dSWB constant or increase it, and increase dLWG. However, a favorable response for all three traits is probably not achievable. Holding the amount of feed provided to the sows constant did not offer an advantage in terms of response over the other strategies.

Selection signature analyses and genome-wide association reveal genomic hotspot regions that reflect differences between breeds of horse with contrasting risk of degenerative suspensory ligament desmitis.

Degenerative suspensory ligament desmitis is a progressive idiopathic condition that leads to scarring and rupture of suspensory ligament fibers in multiple limbs in horses. The prevalence of degenerative suspensory ligament desmitis is breed related. Risk is high in the Peruvian Horse, whereas pony and draft breeds have low breed risk. Degenerative suspensory ligament desmitis occurs in families of Peruvian Horses, but its genetic architecture has not been definitively determined. We investigated contrasts between breeds with differing risk of degenerative suspensory ligament desmitis and identified associated risk variants and candidate genes. We analyzed 670k single nucleotide polymorphisms from 10 breeds, each of which was assigned one of the four breed degenerative suspensory ligament desmitis risk categories: control (Belgian, Icelandic Horse, Shetland Pony, and Welsh Pony), low risk (Lusitano, Arabian), medium risk (Standardbred, Thoroughbred, Quarter Horse), and high risk (Peruvian Horse). Single nucleotide polymorphisms were used for genome-wide association and selection signature analysis using breed-assigned risk levels. We found that the Peruvian Horse is a population with low effective population size and our breed contrasts suggest that degenerative suspensory ligament desmitis is a polygenic disease. Variant frequency exhibited signatures of positive selection across degenerative suspensory ligament desmitis breed risk groups on chromosomes 7, 18, and 23. Our results suggest degenerative suspensory ligament desmitis breed risk is associated with disturbances to suspensory ligament homeostasis where matrix responses to mechanical loading are perturbed through disturbances to aging in tendon (PIN1), mechanotransduction (KANK1, KANK2, JUNB, SEMA7A), collagen synthesis (COL4A1, COL5A2, COL5A3, COL6A5), matrix responses to hypoxia (PRDX2), lipid metabolism (LDLR, VLDLR), and BMP signaling (GREM2). Our results do not suggest that suspensory ligament proteoglycan turnover is a primary factor in disease pathogenesis.

Economic values of reproductive, growth, feed efficiency and carcass traits in Nellore cattle.

A bioeconomic model was developed to calculate the economic value (ev) of reproductive and growth performance, feed efficiency and carcass traits of a seedstock Nellore herd. Data from a full-cycle cattle operation (1,436 dams) located in the Brazilian Cerrado were assessed. The ev was calculated by the difference in profit before and after one-unit improvement in the trait, with others remaining unchanged. The ev was standardized by the phenotypic standard deviation of each trait. Preweaning average daily gain (ADG) was the most economically important trait evaluated (R$ 58.04/animal/year), followed by age at first calving (R$ 44.35), postweaning ADG (R$ 31.43), weight at 450 days (R$ 25.36), accumulated productivity (R$ 21.43), ribeye area (R$ 21.35), calving interval (R$ 19.97), feed efficiency (R$ 15.24), carcass dressing per cent (R$ 8.27), weight at 120 days (R$ 6.22), weight at 365 days (R$ 6.06), weight at weaning (210 days, R$ 5.82), stayability (R$ 5.70) and the probability of early calving (R$ 0.32). The effects of all traits on profits are evidence that their selection may result in the economic and genetic progress of the herd if there is genetic variability.

Predictive assessment of single-step BLUP with linear and non-linear similarity RKHS kernels: A case study in chickens.

Single-step GBLUP (ssGBLUP) to obtain genomic prediction was proposed in 2009. Many studies have investigated ssGBLUP in genomic selection in animals and plants using a standard linear kernel (similarity matrix) called genomic relationship matrix (G). More general kernels should allow capturing non-additive effects as well, whereas GBLUP is based on additive gene action. In this study, we generalized ssBLUP to accommodate two non-linear kernels, the averaged Gaussian kernel (AK) and the recently developed arc-cosine deep kernel (DK). We evaluated the methodology using body weight (BW) and hen-housing production (HHP) traits, recorded on a sample of phenotyped and genotyped commercial broiler chickens. There were, thus, different ssGBLUP models corresponding to G, AK and DK. We used random replication of training (TRN) and testing (TST) layouts at different genotyping rates (20%, 40%, 60% and 80% of all birds) in three selective genotyping scenarios. The selections were genotyping the youngest individuals in the pedigree (YS), random genotyping (RS) and genotyping based on parent average (PA). Predictive abilities were measured using rank correlations between the observed and the predictive phenotypic values in TST for each random partition. Prediction accuracy was influenced by the type of kernel when a large proportion of birds was genotyped. An advantage of non-linear kernels (AK and DK) was more apparent when 60 and 80% of birds had been genotyped. For BW, the lowest rank correlations were obtained with G (0.093 ± 0.015 using RS by 20% genotyped individuals) and the highest values with DK (0.320 ± 0.016 in the PA setting with 80% genotyped individuals). For HHP, the lowest and highest rank correlations were obtained by AK with 20% and 80% genotyped individuals, 0.071 ± 0.016 (in RS) and 0.23 ± 0.016 (in PA) respectively. Our results indicated that AK and DK are more effective than G when a large proportion of the target population is genotyped. Our expectation is that ssGBLUP with AK or DK models can perform even better than G when non-additive genetic effects influence the underlying variability of complex traits.

Nonlinear modeling to describe the pattern of 15 milk protein and nonprotein compounds over lactation in dairy cows.

The protein profile of milk includes several caseins, whey proteins, and nonprotein nitrogen compounds, which influence milk's value for human nutrition and its cheesemaking properties for the dairy industry. To fill in the gap in current knowledge of the patterns of these individual nitrogenous compounds throughout lactation, we tested the ability of a parametric nonlinear lactation model to describe the pattern of each N compound expressed qualitatively (as % of total milk N), quantitatively (in g/L milk), and as daily yield (in g/d). The lactation model was tested on a data set of detailed milk nitrogenous compound profiles (15 fractions-12 protein traits and 3 nonproteins-for each expression mode: 45 traits) obtained from 1,342 cows reared in 41 multibreed herds. Our model was a modified version of Wilmink's model, often used for describing milk yield during lactation because of its reliability and ease of parameter interpretation from a biological point of view. We allowed the sign of the persistency coefficient (parameter c) that explained the variation in the long-term milk component (parameter a) to be positive or negative. We also allowed the short-term milk component (parameter b) to be positive or negative, and we estimated a specific speed of adaptation parameter (parameter k) for each trait rather than assumed a value a priori, as in the original model (k = 0.05). These 4 parameters were included in a nonlinear mixed model with cow breed and parity order as fixed effects, and herd-date as random. Combinations of the positive and negative signs of the b and c parameters allowed us to identify 4 differently shaped lactation curves, all found among the patterns exhibited by the nitrogenous fractions as follows: the "zenith" curve (with a maximum peak; for milk yield and 10 other N traits), the "nadir" curve (with a minimum point; for 20 traits, including almost all those expressed in g/L of milk), the "downward" curve (continuously decreasing; for 14 traits, including almost all those in g/d), and the "upward" curve (continuously increasing; only for κ-casein, in % N). Direct estimation of the k parameters specific to each trait showed the large variability in the adaptation speed of fresh cows and greatly increased the model's flexibility. The results indicated that nonlinear parametric mathematical models can effectively describe the different and complex patterns exhibited by individual nitrogenous fractions during lactation; therefore, they could be useful tools for interpreting milk composition variations during lactation.

Beef production from cull dairy cows: a review from culling to consumption.

Although more than 3 million head of dairy cows enter the food supply chain in the U.S. every year, research on this topic remains limited and scarce. Meat production from dairy cows is a significant component of beef production, accounting for almost 10% of U.S. commercial beef production. Thus, the purpose of this review is to demonstrate the importance of dairy cows as a beef source, and to provide an overview on topics from farm to meat product-culling, marketing, transportation, welfare, body composition and its relationship with lactation particularities, carcass characteristics, meat quality, and traceability. Current scientific evidence has shown that culling a dairy cow at an appropriate time has beneficial effects on cow welfare and, consequently, cow value. During marketing the dairy cow is visually evaluated for health and factors associated with its expected carcass value; thus, marketing a well-conditioned cow will ensure that the animal is fit for transportation and provides high carcass yield. However, limitations such as low body condition score, lameness or mobility problems, and visual defects remain persistent. Even though beef harvest plants accommodate cows in all body composition states, the current carcass grade system does not reflect the mature cow industry needs. Therefore, improvement of the grading system could maximize carcass utilization and increase cow carcass value by recognizing subprimal cuts that could be merchandised as whole muscle cuts. Lastly, implementation of a traceability system would unify information from the farm to harvest assisting the industry in making further advancements.

Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle.

The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, ß-, αS1-, and αS2-CN, and 2 whey proteins, namely ß-lactoglobulin (ß-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between ß-CN and αS1-CN (-0.706) and between ß-CN and κ-CN (-0.735). The application of the MMHC algorithm revealed that κ-CN and ß-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for ß-CN, 30 SNP for αS1-CN, and 14 SNP for αS2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for ß-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.